1. Field of the Invention
The present invention relates to a recognition or diagnosis method primarily using a neural network for diagnosing the presence or absence of a fault in a current operation status by utilizing past operation information of various components of a power supply circuit such as a gas insulated switchgear (GIS) or a transformer. It is applicable to not only various engineering fields but also the medical field, and more generally to the recognition and diagnosis of physical, chemical and biological phenomena.
2. Description of the Related Art
JP-A-2-297074, a diagnosis method utilizing a neural network is proposed. In the method, past status of various types of equipment are previously learned by a neural network having a multi-layer structure. When a new status of the equipment is input, whether the equipment is normal or not is determined based on the learned result. If they are determined to be abnormal, a cause therefor is inferred.
In JP-A-2-272326, detailed description is made regarding mechanical vibration, acoustic vibration and electric oscillation of a rotary machine.
In JP-A-2-100757, a learning method of a parallel neural network is proposed. It is stated therein that a back propagation method is becoming effective as a learning method. A conventional learning method has disadvantages in that the learning does not proceed once it falls in a local minimum and a precision of learning sharply decreases when the amount of data to be learned is excessively large. In the proposed method, more than one neural network is connected in parallel to serially learn in order to improve the efficiency and precision of the learning. Other art related to the present invention are disclosed in U.S. Pat. No. 5,023,045, JP-A-2-206721, JP-A-4-120479, JP-A-3-201079 and JP-A-3-092975.
FIG. 1 shows a conventional neural network. Any number (one in the illustrated example) of hidden layers is provided between an input layer and an output layer. A set of input data X.sub.1, X.sub.2, X.sub.3, . . . , X.sub.n are input to the input layer. In FIG. 1, "1" is also always supplied for adjusting the output from neurons. Products of those inputs and coupling weights W.sub.1, W.sub.2, W.sub.3, . . . , W.sub.n, W.sub.n+1 are inputs U.sub.1 to a neuron Ne. An output from the neuron is V.sub.1. U.sub.1 and V.sub.1 are calculated by equations (1) and (2). ##EQU1##
Similar equations are applied to the inputs and the outputs of other neurons of the hidden layers and the output layers. Only one Na of the neurons of each hidden layer is coupled with "1" for adjusting the output of a neuron connected thereto which the output of the connected is equal to "1" to provide a predetermined coupling weight.
Thus, in the conventional neural network, an operation of learning is essential. The learning regards coupling weights among the neurons that are determined through iterative calculations so that a calculated value approximated to data called teacher data, which is predetermined for input data, is output from the output layer when the input data is applied. For example, learning is utilized to gradually modify the coupling weights through the iterative calculations, to thereby reduce an error which is defined as 0.5 times of a square sum of differences between the teacher data and the calculated output. If the learning proceeds well, the error decreases, but in some cases the error does not decrease and the calculated output is not attained with sufficient precision even after a large amount of learning iteration. Such a case is referred to as the neural network having fallen in a local minimum. In this case, it is not possible to get out of the local minimum whatever number of times of the learning is increased and therefore appropriate coupling weights are not determined.
Even if the calculated output converges to a desired value, a large amount of learning iterations requires a very long time (for example, several hours or more) depending on the scale of the neural network and the teacher data.
In the conventional neural network, the optimum number of hidden layers and the number of neurons are determined by a trial and error method. It is inefficient and improvement thereof has been desired.